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Original Investigation
June 21, 2017

Rethinking the Current American Joint Committee on Cancer TNM Staging System for Medullary Thyroid Cancer

Author Affiliations
  • 1Department of Surgery, Duke University Medical Center, Durham, North Carolina
  • 2Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
  • 3Duke Cancer Institute, Durham, North Carolina
  • 4Duke Clinical Research Institute, Durham, North Carolina
  • 5Deputy Editor, JAMA Surgery
JAMA Surg. Published online June 21, 2017. doi:10.1001/jamasurg.2017.1665
Key Points

Question  How can the current American Joint Committee on Cancer (AJCC) TNM staging system for medullary thyroid cancer be improved to more accurately correlate with survival?

Findings  In a cohort study involving 3315 patients with medullary thyroid cancer, new TNM groupings were proposed that showed more distinct survival differences across TNM groups than was possible with the current AJCC TNM staging system.

Meaning  The current AJCC TNM staging system for medullary thyroid cancer appears to be less than optimal in distinguishing risk of mortality among stage groups, upstaging a significant number of patients to stage IV.

Abstract

Importance  Controversy exists around the American Joint Committee on Cancer (AJCC) TNM staging system for medullary thyroid cancer (MTC). Because of the rarity of the disease and limited available data, the staging system for MTC has been largely extrapolated from staging for differentiated thyroid cancer.

Objectives  To evaluate how well the current (seventh and eighth editions) AJCC TNM staging system correlates with survival for patients with MTC and to suggest a possible revision.

Design, Setting, and Participants  This population-based retrospective cohort analysis used the National Cancer Database to select patients aged 18 years or older diagnosed with MTC in the United States between 1998 and 2012. Patient demographic and tumor characteristics were assessed, and pathologic tumor stages were provided as T, N, and M categories. Recursive partitioning with bootstrapping was used to divide patients by TNM stages into groups with similar survival. The newly identified groupings were validated in a Surveillance, Epidemiology, and End Results (SEER) cohort. Data analysis was conducted between July 17, 2016, and November 11, 2016.

Main Outcomes and Measures  Overall survival and disease-specific survival.

Results  Of the 3315 patients with MTC included in the analysis, 1941 (58.6%) were women. The median (interquartile range) age was 54 (42-65) years, and 2839 (85.6%) self-reported their race/ethnicity as white. The current AJCC TNM staging system classified 941 of these patients (28.4%) as stage I, 907 (27.4%) as stage II, 424 (12.9%) as stage III, and 1043 (31.5%) as stage IV. Recursive partitioning analysis yielded 4 TNM groups: stage I (T1N0-1aM0, T2N0M0), stage II (T1N1bM0, T2N1a-bM0, and T3N0M0), stage III (T3N1a-bM0, T4N0-1bM0), and stage IV (T1-4N0-1bM1). Based on these proposed TNM groupings, 1797 of the 3315 patients (54.2%) were classified as stage I, 684 (20.6%) as stage II, 669 (20.2%) as stage III, and 165 (5.0%) as stage IV. Under the proposed TNM groupings, survival differences across TNM groups were more distinct than under the current AJCC TNM staging system. With stage I as the reference, the hazard ratios of the proposed TNM groupings and the current AJCC TNM staging system were 2.19 (95% CI, 1.37-3.12) vs 1.45 (95% CI, 1.09-1.92) for stage II, 4.20 (95% CI, 2.75-5.75) vs 2.17 (95% CI, 1.59-2.89) for stage III, and 10.97 (95% CI, 5.52-18.57) vs 5.33 (95% CI, 4.13-6.86) for stage IV. In a SEER cohort, the proposed TNM groupings were better at discriminating survival than was the current AJCC TNM staging system.

Conclusions and Relevance  The current AJCC TNM staging system for MTC appears to be less than optimal in distinguishing risk of mortality among stage groups, upstaging a significant number of patients to stage IV. The current AJCC TNM staging system could be improved with the new TNM groupings proposed here for more accurate risk stratification and potential treatment selection.

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